{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt #画图模块\n",
    "import matplotlib.dates as mdates #日期格式处理模块\n",
    "from matplotlib import style #用来自定义图表风格的模块\n",
    "\n",
    "# magic command\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg' #提高分辨率\n",
    "\n",
    "#图片使用中文\n",
    "#from matplotlib.font_manager import FontProperties #画图自定义字体\n",
    "#from pylab import mpl #画图自定义字体\n",
    "#mpl.rcParams['font.sans-serif'] = ['SimHei'] #指定默认字体\n",
    "#mpl.rcParams['font.family'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>96.050</td>\n",
       "      <td>99.980</td>\n",
       "      <td>95.790</td>\n",
       "      <td>99.980</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.980</td>\n",
       "      <td>104.300</td>\n",
       "      <td>104.390</td>\n",
       "      <td>99.980</td>\n",
       "      <td>104.390</td>\n",
       "      <td>0.044109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.390</td>\n",
       "      <td>109.070</td>\n",
       "      <td>109.130</td>\n",
       "      <td>103.730</td>\n",
       "      <td>109.130</td>\n",
       "      <td>0.045407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.130</td>\n",
       "      <td>113.570</td>\n",
       "      <td>114.550</td>\n",
       "      <td>109.130</td>\n",
       "      <td>114.550</td>\n",
       "      <td>0.049666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.550</td>\n",
       "      <td>120.090</td>\n",
       "      <td>120.250</td>\n",
       "      <td>114.550</td>\n",
       "      <td>120.250</td>\n",
       "      <td>0.049760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08-25</th>\n",
       "      <td>3825.759</td>\n",
       "      <td>3848.163</td>\n",
       "      <td>3883.562</td>\n",
       "      <td>3839.972</td>\n",
       "      <td>3883.562</td>\n",
       "      <td>0.015109</td>\n",
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       "    <tr>\n",
       "      <th>2025-08-26</th>\n",
       "      <td>3883.562</td>\n",
       "      <td>3871.471</td>\n",
       "      <td>3888.599</td>\n",
       "      <td>3859.758</td>\n",
       "      <td>3868.382</td>\n",
       "      <td>-0.003909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08-27</th>\n",
       "      <td>3868.382</td>\n",
       "      <td>3869.612</td>\n",
       "      <td>3887.198</td>\n",
       "      <td>3800.350</td>\n",
       "      <td>3800.350</td>\n",
       "      <td>-0.017587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08-28</th>\n",
       "      <td>3800.350</td>\n",
       "      <td>3796.711</td>\n",
       "      <td>3845.087</td>\n",
       "      <td>3761.422</td>\n",
       "      <td>3843.597</td>\n",
       "      <td>0.011380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08-29</th>\n",
       "      <td>3843.597</td>\n",
       "      <td>3842.823</td>\n",
       "      <td>3867.606</td>\n",
       "      <td>3839.206</td>\n",
       "      <td>3857.927</td>\n",
       "      <td>0.003728</td>\n",
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       "            Preclose      Open   Highest    Lowest     Close    Return\n",
       "Day                                                                   \n",
       "1990-12-19       NaN    96.050    99.980    95.790    99.980       NaN\n",
       "1990-12-20    99.980   104.300   104.390    99.980   104.390  0.044109\n",
       "1990-12-21   104.390   109.070   109.130   103.730   109.130  0.045407\n",
       "1990-12-24   109.130   113.570   114.550   109.130   114.550  0.049666\n",
       "1990-12-25   114.550   120.090   120.250   114.550   120.250  0.049760\n",
       "...              ...       ...       ...       ...       ...       ...\n",
       "2025-08-25  3825.759  3848.163  3883.562  3839.972  3883.562  0.015109\n",
       "2025-08-26  3883.562  3871.471  3888.599  3859.758  3868.382 -0.003909\n",
       "2025-08-27  3868.382  3869.612  3887.198  3800.350  3800.350 -0.017587\n",
       "2025-08-28  3800.350  3796.711  3845.087  3761.422  3843.597  0.011380\n",
       "2025-08-29  3843.597  3842.823  3867.606  3839.206  3857.927  0.003728\n",
       "\n",
       "[8473 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入处理\n",
    "data = pd.read_csv('F:/Python2025/000001.csv')\n",
    "data['Day'] = pd.to_datetime(data['Day'],format='%Y/%m/%d')\n",
    "data.set_index('Day',inplace=True)\n",
    "data['Close'] = pd.to_numeric(data['Close'],errors='coerce')\n",
    "data['Preclose'] = data['Close'].shift(1)\n",
    "data['Return'] = (data['Close'] - data['Preclose'])/data['Preclose'] \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1995-01-31</th>\n",
       "      <td>-0.131631</td>\n",
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       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
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       "      <th>1995-03-31</th>\n",
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       "      <th>1995-04-30</th>\n",
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       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>0.207922</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>2024-08-31</th>\n",
       "      <td>-0.032849</td>\n",
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       "      <th>2024-09-30</th>\n",
       "      <td>0.173908</td>\n",
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       "      <th>2024-10-31</th>\n",
       "      <td>-0.016986</td>\n",
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       "      <th>2024-11-30</th>\n",
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       "              Return\n",
       "Day                 \n",
       "1995-01-31 -0.131631\n",
       "1995-02-28 -0.023694\n",
       "1995-03-31  0.177803\n",
       "1995-04-30 -0.103552\n",
       "1995-05-31  0.207922\n",
       "...              ...\n",
       "2024-08-31 -0.032849\n",
       "2024-09-30  0.173908\n",
       "2024-10-31 -0.016986\n",
       "2024-11-30  0.014218\n",
       "2024-12-31  0.007608\n",
       "\n",
       "[360 rows x 1 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_new = data['1995-01-01':'2024-12-31'].copy()\n",
    "Month_data = data_new.resample('ME')['Return'].apply(lambda x: (1+x).prod()-1).to_frame()\n",
    "Month_data"
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   "cell_type": "markdown",
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   "source": [
    "画图"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       " <defs>\n",
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      ],
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.clf() \n",
    "plt.close('all') \n",
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "\n",
    "ax.plot('Return', #图片数据\n",
    "        '.-', #图片类型\n",
    "color = 'r', #图片颜色\n",
    "label = 'Monthly Return', #图片标签(最好不写中文)\n",
    "linewidth = 1, #图片线宽\n",
    "data = Month_data) #图片数据来源\n",
    "ax.set_title(\"China's Stock Market Monthly Return\") #图片标题\n",
    "ax.set_ylabel('Return') #图片y轴标签\n",
    "ax.set_xlabel('Year') #图片x轴标签\n",
    "\n",
    "#设置x轴的日期显示格式\n",
    "data_format = mdates.DateFormatter('%Y')\n",
    "ax.xaxis.set_major_formatter(data_format)\n",
    "ax.xaxis.set_major_locator(mdates.YearLocator())\n",
    "\n",
    "#转置x轴的日期显示格式\n",
    "plt.xticks(rotation = 270)\n",
    "\n",
    "#添加图例\n",
    "plt.legend(loc='upper right',fontsize = 8)\n",
    "fig.savefig('F:/Python2025/China_Stock_Market_Monthly_Return.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args: 'float | ArrayLike | str', scalex: 'bool' = True, scaley: 'bool' = True, data=None, **kwargs) -> 'list[Line2D]'\n",
      "    Plot y versus x as lines and/or markers.\n",
      "\n",
      "    Call signatures::\n",
      "\n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "\n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "\n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "\n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "\n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "\n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "\n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "\n",
      "\n",
      "    **Plotting labelled data**\n",
      "\n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "\n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "\n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFrame` or a structured numpy array.\n",
      "\n",
      "\n",
      "    **Plotting multiple sets of data**\n",
      "\n",
      "    There are various ways to plot multiple sets of data.\n",
      "\n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "\n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "\n",
      "    - If *x* and/or *y* are 2D arrays, a separate data set will be drawn\n",
      "      for every column. If both *x* and *y* are 2D, they must have the\n",
      "      same shape. If only one of them is 2D with shape (N, m) the other\n",
      "      must have length N and will be used for every data set m.\n",
      "\n",
      "      Example:\n",
      "\n",
      "      >>> x = [1, 2, 3]\n",
      "      >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n",
      "      >>> plot(x, y)\n",
      "\n",
      "      is equivalent to:\n",
      "\n",
      "      >>> for col in range(y.shape[1]):\n",
      "      ...     plot(x, y[:, col])\n",
      "\n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "\n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "\n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also, this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "\n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to ``range(len(y))``.\n",
      "\n",
      "        Commonly, these parameters are 1D arrays.\n",
      "\n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "\n",
      "        These arguments cannot be passed as keywords.\n",
      "\n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "\n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "\n",
      "        This argument cannot be passed as keyword.\n",
      "\n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "\n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
      "            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            ``plot('n', 'o', '', data=obj)``.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    list of `.Line2D`\n",
      "        A list of lines representing the plotted data.\n",
      "\n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, default: True\n",
      "        These parameters determine if the view limits are adapted to the\n",
      "        data limits. The values are passed on to\n",
      "        `~.axes.Axes.autoscale_view`.\n",
      "\n",
      "    **kwargs : `~matplotlib.lines.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "\n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "\n",
      "        If you specify multiple lines with one plot call, the kwargs apply\n",
      "        to all those lines. In case the label object is iterable, each\n",
      "        element is used as labels for each set of data.\n",
      "\n",
      "        Here is a list of available `.Line2D` properties:\n",
      "\n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n",
      "        alpha: scalar or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `~matplotlib.transforms.BboxBase` or None\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: :mpltype:`color`\n",
      "        dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
      "        dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `~matplotlib.figure.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gapcolor: :mpltype:`color` or None\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
      "        markeredgecolor or mec: :mpltype:`color`\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: :mpltype:`color`\n",
      "        markerfacecoloralt or mfcalt: :mpltype:`color`\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n",
      "        mouseover: bool\n",
      "        path_effects: list of `.AbstractPathEffect`\n",
      "        picker: float or callable[[Artist, Event], tuple[bool, dict]]\n",
      "        pickradius: float\n",
      "        rasterized: bool\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
      "        solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
      "        transform: unknown\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "\n",
      "    .. note::\n",
      "\n",
      "        This is the :ref:`pyplot wrapper <pyplot_interface>` for `.axes.Axes.plot`.\n",
      "\n",
      "    **Format Strings**\n",
      "\n",
      "    A format string consists of a part for color, marker and line::\n",
      "\n",
      "        fmt = '[marker][line][color]'\n",
      "\n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "\n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "\n",
      "    **Markers**\n",
      "\n",
      "    =============   ===============================\n",
      "    character       description\n",
      "    =============   ===============================\n",
      "    ``'.'``         point marker\n",
      "    ``','``         pixel marker\n",
      "    ``'o'``         circle marker\n",
      "    ``'v'``         triangle_down marker\n",
      "    ``'^'``         triangle_up marker\n",
      "    ``'<'``         triangle_left marker\n",
      "    ``'>'``         triangle_right marker\n",
      "    ``'1'``         tri_down marker\n",
      "    ``'2'``         tri_up marker\n",
      "    ``'3'``         tri_left marker\n",
      "    ``'4'``         tri_right marker\n",
      "    ``'8'``         octagon marker\n",
      "    ``'s'``         square marker\n",
      "    ``'p'``         pentagon marker\n",
      "    ``'P'``         plus (filled) marker\n",
      "    ``'*'``         star marker\n",
      "    ``'h'``         hexagon1 marker\n",
      "    ``'H'``         hexagon2 marker\n",
      "    ``'+'``         plus marker\n",
      "    ``'x'``         x marker\n",
      "    ``'X'``         x (filled) marker\n",
      "    ``'D'``         diamond marker\n",
      "    ``'d'``         thin_diamond marker\n",
      "    ``'|'``         vline marker\n",
      "    ``'_'``         hline marker\n",
      "    =============   ===============================\n",
      "\n",
      "    **Line Styles**\n",
      "\n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "\n",
      "    Example format strings::\n",
      "\n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "\n",
      "    **Colors**\n",
      "\n",
      "    The supported color abbreviations are the single letter codes\n",
      "\n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "\n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "\n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-12-31</th>\n",
       "      <td>-0.142899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-12-31</th>\n",
       "      <td>0.651425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-31</th>\n",
       "      <td>0.302153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-12-31</th>\n",
       "      <td>-0.039695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31</th>\n",
       "      <td>0.191750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>0.517277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>-0.206180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>-0.175167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>0.102670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>-0.153997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>-0.083253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>1.304334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>0.966593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>-0.653941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>0.799825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>-0.143131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>-0.216753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>0.031695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>-0.067493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>0.528691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.094136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>-0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>0.065578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>-0.245911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>0.223032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.138667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>0.048001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>-0.151251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>-0.037007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>0.126668</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              Return\n",
       "Day                 \n",
       "1995-12-31 -0.142899\n",
       "1996-12-31  0.651425\n",
       "1997-12-31  0.302153\n",
       "1998-12-31 -0.039695\n",
       "1999-12-31  0.191750\n",
       "2000-12-31  0.517277\n",
       "2001-12-31 -0.206180\n",
       "2002-12-31 -0.175167\n",
       "2003-12-31  0.102670\n",
       "2004-12-31 -0.153997\n",
       "2005-12-31 -0.083253\n",
       "2006-12-31  1.304334\n",
       "2007-12-31  0.966593\n",
       "2008-12-31 -0.653941\n",
       "2009-12-31  0.799825\n",
       "2010-12-31 -0.143131\n",
       "2011-12-31 -0.216753\n",
       "2012-12-31  0.031695\n",
       "2013-12-31 -0.067493\n",
       "2014-12-31  0.528691\n",
       "2015-12-31  0.094136\n",
       "2016-12-31 -0.123062\n",
       "2017-12-31  0.065578\n",
       "2018-12-31 -0.245911\n",
       "2019-12-31  0.223032\n",
       "2020-12-31  0.138667\n",
       "2021-12-31  0.048001\n",
       "2022-12-31 -0.151251\n",
       "2023-12-31 -0.037007\n",
       "2024-12-31  0.126668"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quarterly_data = data_new.resample('QE')['Return'].apply(lambda x:(1+x).prod()-1).to_frame()\n",
    "Yearly_data = data_new.resample('YE')['Return'].apply(lambda x:(1+x).prod()-1).to_frame()\n",
    "Quarterly_data \n",
    "Yearly_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12158915002289622"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yearly_data['1995':'2023']['Return'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股票市场月度收益率的均值 0.73 %\n"
     ]
    }
   ],
   "source": [
    "print('中国股票市场月度收益率的均值',Month_data['1995':'2023']['Return'].mean().round(4)*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股票市场收益率年化均值 12.16 %\n"
     ]
    }
   ],
   "source": [
    "print('中国股票市场收益率年化均值',Yearly_data['1995':'2023']['Return'].mean().round(4)*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12158915002289622"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(Yearly_data['1995':'2023']['Return'])"
   ]
  }
 ],
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